In many environments, it is desirable, in fact necessary, to reduce the amplitude of noise, vibrations, and/or other interfering signals. The prior art has attempted to accomplish this reduction using a variety of techniques, both passive and active.
Passive reduction or attenuation is generally accomplished by disposing one or more layers of barrier, absorbing, and/or damping materials between the source of the noise or vibration and the area where a reduced or attenuated noise level is desired. While effective in some situations, passive attenuation systems are often unsuitable for applications where size, weight, and/or cost considerations prevent the use of attenuating materials.
Other prior art techniques have focussed on active signal reduction techniques such as Active Noise Cancellation (ANC). Active Noise Cancellation has received a considerable amount of interest in a variety of signal cancellation applications, e.g., air ducts, exhaust fans, zonal quieting, head phones, vibration cancellation in structures, and echo cancellation in electronic signal communications. The active reduction of sound waves in the audible range is performed by processing the electrical cancellation signals at a rate greater than the rate of propagation of those sound waves in a particular propagation medium. In the time it takes for a sound wave to propagate from a location where the sound is measured to a second location where it may be cancelled, there is time to sample the sound wave signal, process that information in a processing circuit, and produce a signal to drive an actuator to introduce a cancelling signal 180.degree. out-of-phase and equal in amplitude to the propagating sound wave.
The function block diagram shown in FIG. 1(a) is useful in explaining basic principles of active noise cancellation systems or vibration cancellation systems. A noise or vibrational disturbance 10 is detected by a suitable sensor 12. The sensor 12 converts the noise or vibration 10 into an electrical signal which is processed in some fashion in a controller 14. The controller 14 determines a cancellation signal, typically the inverse of the sensed noise or vibration signal, and uses this cancellation signal to drive an actuator 16. In the case of acoustic noise, the actuator 16 is simply a speaker. If the cancellation is appropriately timed, the original noise signal is cancelled by an output signal generated by the actuator 16. This cancellation is represented mathematically as a summation of the sensed and cancellation signals at a summer 18.
A graphic depiction of the cancellation process is provided in FIG. 1(b). The noise signal represented by a waveform signal "a" is essentially cancelled by another waveform signal "b" of equal amplitude but having a phase difference of 180.degree.. The sum of these two waveforms leaves only a residual signal "c".
Systems for actively cancelling repetitive noise and vibration have been proposed for example in Chaplin, U.S. Pat. Nos. 4,153,815; 4,490,841 and 4,654,871; as well as Warnaka et al, U.S. Pat. No. 4,562,589; and Ziegler, Jr., U.S Pat. No. 4,878,188.
In U.S. Pat. Nos. 4,153,815 and 4,654,871, Chaplin describes the use of a synchronizing timing generator to provide cancellation of a repetitive noise. Initially, a noise or vibration signal is detected and analyzed so that a cancelling signal waveform can be generated. Once the cancelling waveform has been determined, a controller and pulse generators attempt to synchronize the timing of the cancellation signal so that the noise or vibration is cancelled. Any remaining noise is fed back to the controller as an error signal. The noise signal is divided into multiple intervals, and the amplitude of the cancelling signal is adjusted in each interval in response to the sign or amplitude of the error signal.
In U.S. Pat. No. 4,490,841, Chaplin describes the use of Fourier transforms to process signals in the frequency domain. In this system, repetitive noise or vibration signals are cancelled by individually synchronizing the output of different frequency components of the cancelling signal based on a repetition rate sensed at the noise source. Fourier transforms are used to identify and quantify the discrete frequency components that contribute most significantly to the noise signal. These discrete frequency components are modified separately in order to adapt the cancelling waveform to the detected noise signals. The modified frequency components are inverse Fourier transformed back into the time domain to produce a cancellation signal for an output actuator.
The use of adaptive filters to accelerate the adaptation of active noise cancellation systems is suggested for example by Warnaka in U.S. Pat. No. 4,562,589. Widrow et al., in "Adaptive Noise Cancelling Principles and Applications," Proceedings of IEEE, Vol. 63, No. 12, 12/75, pp. 1692-1716, uses a multiple weight, adaptive finite impulse response (FIR) filter to actively cancel noise signals. A previously determined, reference noise signal is used to tune or adapt the coefficients of the adaptive filter. The output signal from the filter is subtracted from the actual noise signal. Any detected residual noise or error is fed back to adjust the filter coefficients. A requirement of the Widrow system is that the reference signal be within 90.degree. in-phase of the error signal.
A variation of the adaptive filter of the Widrow model is disclosed in the Ziegler, Jr. U.S. Pat. No. 4,878,188. The adaptive filtering system includes for each frequency to be cancelled, a sine and cosine generator, responsive to a timing/synchronizing signal, for providing inputs to two adaptive filters whose outputs are summed to provide a cancellation signal.
A particular application of noise cancellation is found in audio headphones or headsets, as disclosed, for example, in U.S. Pat. Nos. 4,455,675 Bose et al. and 4,494,074 to Bose. In these patents, a microphone is located in a small cavity in the headphones between the diaphragm and the ear canal adjacent to the diaphragm. The microphone generates a feedback signal that is combined with the input electrical signal to the headphones. The feedback signal corresponds to the sum of ambient acoustic noise and the sound produced by the headphone driver in that cavity. The sum of the feedback signal and the audio signal provides an error signal which is used to generate a compensation signal.
Unfortunately, the prior art signal cancelling/reduction systems are complex, relatively slow, and inflexible. For example, many of the systems described above process signals in the frequency domain. As a result, three Fourier transformations must be calculated for each waveform: a first conversion of the detected signal into the frequency domain, a second conversion of the detected residual noise into the frequency domain, and a third conversion of the cancellation signal back into the time. domain. Obviously, the computational time required to calculate these transformations is considerable.
Another deficiency of the prior art is the limited ability to adapt quickly to or predict changes in the character of the noise waveform. Undue reliance is placed on the assumption that the signal to be cancelled can be characterized as periodic or repetitive in nature. Thus, only those repetitive noises or vibrations that can be characterized and/or analyzed before the cancellation process begins, e.g., by using a reference or model noise signal, can be cancelled. Many of the prior art systems require timing and synchronization signals in order to accomplish noise cancellation. A periodic and/or random signals that cannot be predicted ahead of time cannot be cancelled effectively. A periodic signals are signals that do not repeat themselves at fixed intervals, but occur in unknown time and space intervals, e.g., a signal that has a random, time varying character.
Moreover, the prior art systems restrict their signal analysis either to a limited number of discrete periodic frequency components or to limited frequency bandwidths of the signal to be cancelled. This assumption is not acceptable in situations where the noise or vibration signals are not known in advance, where the frequency components of those signals vary over time, or where the frequencies of interest exceed the operating bandwidths of those systems.
One of the difficulties in cancelling noise or any other type of undesired signal is accurately characterizing that noise. Within the specific frequency band of the desired signal, there are noise components that are extremely difficult to isolate or to accurately predict given the random nature of some types of noise signals. For example, environmental noise such as wind, is random and is difficult to effectively predict, model, or isolate.
The difficulty encountered in cancelling random noise also presents a problem in the broader context of signal enhancement. Signal enhancement refers generally to improving the distinguishability of a desired signal in a received signal by increasing the signal-to-noise ratio and improving the quality of the desired signal. If random noise is present, the gain and/or quality of a desired signal can only be improved to limited extent. For example, increasing the signal gain increases not only the gain of the desired signal but also the gain of the random noise. Thus, signal enhancement ultimately requires that the received signal be filtered in some way to remove undesired, random noise components.
Accordingly, there is a need for a simplified signal processing system that enhances a signal in the process of quickly and accurately filtering undesired, unpredictable signals from a desired frequency band. Moreover, when applying such a signal enhancement procedure to active noise cancellation, there is a need for a signal processing system that effectively characterizes and isolates the undesired, unpredictable signal components in the frequency band of the desired signal and actively cancels those undesired signals.